{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--clf---\n",
      "GaussianNB(priors=None)\n",
      "---clf--\n",
      "BEGIN_IMAGE_f9825uweof8jw9fj4r8{\"bytes\": 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MDkyZNZuHAhLS0t3HbbbQWPf/LJJzn22GM566yzOOigg5g5cyZnnnkm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\\n\", \"name\": \"test.png\", \"format\": \"png\"}END_IMAGE_0238jfw08fjsiufhw8frs\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/python\n",
    "\n",
    "\"\"\" Complete the code in ClassifyNB.py with the sklearn\n",
    "    Naive Bayes classifier to classify the terrain data.\n",
    "    \n",
    "    The objective of this exercise is to recreate the decision \n",
    "    boundary found in the lesson video, and make a plot that\n",
    "    visually shows the decision boundary \"\"\"\n",
    "\n",
    "\n",
    "from prep_terrain_data import makeTerrainData\n",
    "from class_vis import prettyPicture, output_image\n",
    "from ClassifyNB import classify\n",
    "\n",
    "import numpy as np\n",
    "import pylab as pl\n",
    "\n",
    "\n",
    "features_train, labels_train, features_test, labels_test = makeTerrainData()\n",
    "\n",
    "### the training data (features_train, labels_train) have both \"fast\" and \"slow\" points mixed\n",
    "### in together--separate them so we can give them different colors in the scatterplot,\n",
    "### and visually identify them\n",
    "grade_fast = [features_train[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]==0]\n",
    "bumpy_fast = [features_train[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]==0]\n",
    "grade_slow = [features_train[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]==1]\n",
    "bumpy_slow = [features_train[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]==1]\n",
    "\n",
    "\n",
    "# You will need to complete this function imported from the ClassifyNB script.\n",
    "# Be sure to change to that code tab to complete this quiz.\n",
    "clf = classify(features_train, labels_train)\n",
    "\n",
    "\n",
    "### draw the decision boundary with the text points overlaid\n",
    "prettyPicture(clf, features_test, labels_test)\n",
    "output_image(\"test.png\", \"png\", open(\"test.png\", \"rb\").read())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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